Fault diagnosis method of rolling bearing based on deep belief network
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A method based on the theory of deep learning and feature extraction and a fault diagnosis model of a rolling bearing based on deep belief network are proposed in this study considering the complex, nonlinear, and non-stationary vibration signal of the rolling bearing. To some extent, the method avoids the complex structure of deep neural network and can be easily trained. Experimental results show that the recognition rate of the method reaches 100 %. The method can identify various types of faults accurately and has good fault diagnosis capability, which can provide the convenience for maintenance.
KeywordsDeep belief network Fault diagnosis Restricted Boltzmann machine Rolling bearing
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